According to a global survey from 2024, the age group 25 to 34 is most likely to use chatbots when visiting brand websites. Approximately 60 percent of users within this age group utilized chatbots on a direct-to-consumer (D2C) site. The age group between 35 and 44 ranked second, with nearly 56 percent of respondents. Those aged 55 and 64 were the least likely to use this type of software application.

According to a global 2022 survey, Indian consumers appreciated customer service via chatbots the most, with 36 percent of them finding it useful when shopping on mobile devices. Respondents from the United Arab Emirates and Indonesia followed with 30 percent each, while 27 percent of Mexican shoppers had the same opinion. Scandinavian respondents showed the biggest skepticism regarding the use of this AI tool in mobile shopping, as only eight percent of consumers in both Denmark and Sweden valued chatbots.

Chatbots drive conversational commerce

By simulating natural language, chatbots go under so-called conversational commerce, a shopping channel expected to generate increasing revenue in the upcoming years. All players, from marketplaces to online e-commerce brands, implemented chatbots to automate pre-and post-sale services. In Europe, one in five direct-to-consumer (D2C) e-commerce companies planned to invest in Artificial Intelligence (AI) and chatbots, a survey from 2022 revealed.

Messaging apps in conversational commerce

Even more than chatbots, established messaging apps such as WhatsApp or chat apps connected to social media are the main tools for conversational commerce. In Southeast Asia, 27 percent of online consumers used Facebook for this type of shopping in 2022. Besides product recommendations, messaging apps are used for information on delivery as an alternative to email or SMS. In 2021, WhatsApp remained the third-most preferred channel for delivery notifications in Europe.

According to a 2024 survey, over eight in ten Spanish consumers would engage with chatbots powered with generative AI technology to receive support. Italians followed with 81 percent while another 79 percent of Irish shoppers would use Gen AI chatbots for an element of customer service.

The Global Chatbot Market is Expanding Due To Increasing Demand for Messaging Bot Applications and Businesses' Growing Adoption of Consumer Analytics. These AI Assistants, or Bots, Function As Digital Assistants, Using AI and Natural Language Processing To Understand and Respond To Human Needs. Automated Chatbots Integrated With Messaging Applications Enhance User Experience and Generate Higher Returns for Businesses. They Also Provide 24/7 Customer Support, Managing Large Volumes of Requests Simultaneously. The Conversational AI Market, Segmented by End-User Vertical and Geography, Shows Significant Growth in the Retail Sector and the Asia-Pacific Region.

As per Cognitive Market Research's latest published report, the Global Chatbot market size was USD 3.02 Billion in 2022 and it is forecasted to reach USD 24.58 Billion by 2030. Chatbot Industry's Compound Annual Growth Rate will be 21.58% from 2023 to 2030. Factors Impacting on Chatbot Market

High integration of chatbot in various industrial verticals:

Use of chatbots is rising exponentially in both the business sector as well as in consumer market. It is an instant messaging app that creates natural conversations between businesses and customers. The demand for chatbot has increased in recent years attributed to the rising inclination of people across the world towards online shopping. In online shopping platforms, sales team uses chatbots to answer non-complex product questions which helps in improving the satisfaction level and convenience of customers.

Moreover, the world is moving rapidly towards digitalization. Amid COVID-19 pandemic, the world has been turned totally into digital world. Hence, healthcare industry, like all other industries have started using chatbot aggressively which helps in connecting patients with hospitalists for general diagnosis and treatment. It also allows in scheduling appointments with physicians without needing to travel to the hospital.

Chatbot have been connected through websites, mobile applications, along with social media platforms which further drives the growth of market. As AI implementation in chatbot is rising, it is revolutionizing the business processes in multiple industries. AI-powered chatbot has thus no limits for its usage in various sectors, including BFSI, telecommunication, e-commerce, and others accrediting the growth market across the world.

Increasing need for customer analytics and emergence of messenger apps to drive the market


Restraining Factor of Chatbot Market

Drawbacks regarding the full understanding of natural language:

In order to ensure that chatbot is providing correct and relevant information to the customers, it must be updated with the correct information. However, people in today's world widely uses shortforms out of their habit for speedy responses. Such kind of slangs or misspellings are frequently misunderstood by these chatbots. Hence, inability in understanding this kind of natural language may hamper the growth of chatbot market. However, rising use of cloud services by various enterprises will help chatbot to retrieve huge amount of data from the cloud which will enhance the understanding of natural language and further stimulating the growth of chatbot market.

Current Trends in Chatbot market:

AI chatbots with high emotional intelligence will drives the market in coming years:

Using artificial intelligence and real time data, chatbot is now able to do sentiment analysis by using facial emotion recognition, eye tracking technology and video interactions in real-time. This allows it to understand the mood, pitch, and feelings and customize their responses to deliver custom-made communication.

Thus, it will not be wrong to say that AI-powered chatbot is going to enhance values in business sectors by providing limitless applications in large, medium and small enterprises. When more companies use the cloud, their ability to manage customer interactions, data management, and internal communication effectively will greatly increase their business agility without having to worry about increased infrastructure costs or security risks.

What is the impact of the COVID-19 pandemic on Chatbot Market:

Advent of COVID-19 pandemic has reshaped the lives of people across the globe by changing the way of work, shop, and learn. Every sector has been impacted due to the sudden out-break of pandemic. Lockdowns were announced and many customer service centers were closed. Disruption in supply chain occurred and online services failed to handle additional volumes effectively. Hence, to handle this chaos effectively, companies started investing in new technologies to provide additional support and allow workers to adapt to work-from-home setups.

Lockdown during year 2020, embraced digital world like never before. Thus, digital literacy rate during the pandemic increases exponentially which results in stimulation of chatbot use. Retail businesses increases the use of chatbot during COVID-19 to fulfil consumer needs and giving retailers a competitive edg...

Get insights through powerful and well researched Chatbot Statistics that you need get through before implementing chatbot in 2024 for your business

One of the reasons behind AI-powered customer service is the preference for conversational AI over phone calls. In 2024, 82 percent of consumers stated they would use a chatbot instead of waiting for a customer representative to take their call. An outstanding 96 percent of surveyed shoppers believed that more companies should opt for chatbots over traditional customer support services.

This dataset contains responses from a survey conducted for a master's thesis at Erasmus University Rotterdam. The survey investigated how consumer perceptions of privacy and trust in interactions with centralized versus decentralized AI-powered chatbots influence customer satisfaction. The survey included a predetermined simulated conversation with an AI-powered chatbot.Purpose of the Study:The main research question addressed in this study is: "How do consumer perceptions of privacy and trust in interactions with centralized versus decentralized AI-powered chatbots influence customer satisfaction?" The study aims to compare the differences in customer satisfaction, privacy concerns, and trust between centralized and decentralized AI-powered chatbots.Data Description:This dataset includes responses from 175 participants after data cleaning and removal of incomplete and biased responses. Participants were randomly assigned to one of three groups:Unaware of the chatbot typeInformed they would interact with a centralized chatbotInformed they would interact with a decentralized chatbotVariables:Customer Satisfaction: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer Privacy Concerns: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer Trust in AI-Powered Chatbots: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer AI Familiarity: Measured with Likert scale questions regarding prior usage and understanding of AI technology on a 5-point scale from Strongly disagree to Strongly agree.Demographic Information: Age group, gender, highest education finished, nationality, and occupation.Chatbot Type: Categorical variable with values: 0 for not aware, 1 for aware of interacting with a centralized chatbot, and 2 for aware of interacting with a decentralized chatbot.Usage Notes:The dataset is provided in a XLSX file format and includes all necessary variables for analysis. The dataset can be used to conduct various statistical analyses, including descriptive statistics, hypothesis testing, and regression analysis.

The chatbot market size is projected to grow from $ 5.84 billion in 2024 to $61.97 billion by 2035, representing a CAGR of 23.94% during the forecast period 2024-2035.

According to Cognitive Market Research, the global AI Chatbots market size will be USD 474.88 million in 2024 and will expand at a compound annual growth rate (CAGR) of 19.46% from 2024 to 2031.

The North America AI Chatbots market size was USD 1,336.33 Million in 2019 and it is expected to reach USD 12,529.12 Million in 2031.
The Europe AI Chatbots market size was USD 906.17 Million in 2019 and it is expected to reach USD 8,950.15 Million in 2031.
The Asia Pacific AI Chatbots market size was USD 831.48 Million in 2019 and it is expected to reach USD 8,776.80 Million in 2031.
The South America AI Chatbots market size was USD 146.70 Million in 2019 and it is expected to reach USD 1,341.50 Million in 2031.
The Middle East and Africa AI Chatbots market size was USD 74.69 Million in 2019 and it is expected to reach USD 662.37 Million in 2031.

Market Dynamics of AI Chatbots Market

Key Drivers for AI Chatbots Market

Advancements in AI and NLP Technologies are propelling the growth of AI chatbots Market

The rapid evolution of Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies has been a primary driver of growth in the global AI chatbot market. These advancements have significantly enhanced chatbot capabilities, enabling them to provide more human-like, context-aware, and efficient interactions. The introduction of deep learning models, transformer-based architectures, and generative AI has revolutionized how chatbots understand, process, and respond to human language. These are the reasons why players across the industry are focusing more on creating intuitive chatbot solutions. For instance, in October 2024, JSW and MG Motor collaborated with Google Cloud to launch gen Al chatbots. These are capable of understanding complex queries and responding with simple words to ensure the customer is satisfied with the response. Overall, the advancements in AI and NLP technologies have made AI chatbots more intelligent, efficient, and scalable, driving their widespread adoption across multiple industries. As AI continues to evolve with enhanced contextual learning, emotional intelligence, and ethical AI frameworks, the chatbot market is expected to experience sustained growth, further transforming customer service, automation, and digital engagement on a global scale.

Key Restraints for AI Chatbots Market

Integration challenges and data privacy concerns are restraining the growth of AI chatbots market

Despite the rapid adoption of AI chatbots across industries, integration challenges and data privacy concerns are key restraints limiting market growth. As businesses deploy AI chatbots to enhance customer engagement and automate processes, they often face complexities in integrating these solutions with existing enterprise systems, databases, and applications. Additionally, increasing concerns about data security, regulatory compliance, and ethical AI usage are raising barriers to widespread adoption. For instance, in April 2023, OpenAI taken ChatGPT offline in Italy after the government's Data Protection Authority temporarily banned the chatbot and launched a probe over the artificial intelligence application's suspected breach of privacy rules. These issues presents challenges for chatbot creators to align with the data security norms of the countries to function appropriately Overall, while AI chatbots offer immense potential for customer service automation and business efficiency, integration challenges and data privacy concerns remain significant roadblocks to their widespread adoption. Overcoming these restraints will require standardized AI frameworks, improved interoperability, stronger data security measures, and enhanced regulatory compliance strategies to unlock the full potential of AI chatbots Introduction of AI Chatbots Market

The global AI chatbots market is experiencing rapid expansion, fueled by advancements in artificial intelligence, natural language processing (NLP), and machine learning. Businesses across industries are adopting chatbots to enhance customer service, automate responses, and improve user engagement. The growing demand for AI-driven automation and personalized interactions is expected to continue driving the market forward. AI chatbots can be categorized into multiple types based on their functionality and capabilities. Q&A chatbots are the most common, designed to answer predefined questions based on r...

As of 2024, approximately 46 percent of the surveyed companies in the United States of America claim to use artificial intelligence (AI) tools such as ChatGPT, virtual assistants, and chatbots in their activities. About 15 percent of the respondents are still unsure whether the tools are being used or not.

Vietnam chatbot market size is projected to exhibit a growth rate (CAGR) of 24.62% during 2024-2032. The market is being driven by several key factors, including an increasing need for improved customer service, a rising trend in using messaging platforms to offer efficient customer solutions, and a growing uptake of over-the-top (OTT) platforms for streaming movies, series, and documentaries.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on type, product, application, organization size, and vertical.

The Chatbot Market is projected to grow at 30.0% CAGR, reaching $29.5 Billion by 2029. Where is the industry heading next? Get the sample report now!

Introduction

Global Chatbots for Mental Health & Therapy Market size is expected to be worth around US$ 2.2 Billion by 2033, from US$ 1.3 Billion in 2023, growing at a CAGR of 5.6% during the forecast period from 2024 to 2033. In 2023, North America led the market, achieving over 41.6% share with a revenue of US$ 0.5 Billion.

This growth is fueled by several key drivers, including the increasing prevalence of mental health conditions globally, advancements in natural language processing (NLP) technology, and the growing demand for accessible mental health support solutions.

Rising awareness and the reduction of stigma surrounding mental health issues have encouraged individuals to seek help, accelerating the adoption of chatbots. These tools provide a private and non-judgmental platform for users to express their emotions and receive support, making them an attractive alternative for those hesitant to pursue traditional therapy. Additionally, chatbots are scalable and accessible, offering mental health support to underserved and remote areas with limited healthcare resources. Their 24/7 availability ensures immediate assistance, irrespective of location or time, further enhancing their appeal.

Despite these advantages, the market faces notable challenges. Current chatbot technologies still struggle to fully replicate human emotions and address complex mental health issues, leading to potential shortcomings in the quality of care provided. Advancements in NLP have improved capabilities but remain insufficient to handle nuanced mental health scenarios, which can sometimes result in inappropriate responses. Ethical concerns around data privacy and user consent also pose significant hurdles, emphasizing the need for transparency and user empowerment in chatbot development.

Recent developments highlight growing investment and innovation within the sector. Companies such as Wysa and Woebot Health have secured substantial funding to improve chatbot functionalities and expand their market presence. Additionally, the integration of chatbots with wearable devices and other digital health tools represents a significant trend, enabling more personalized and context-aware mental health support.

In summary, the Chatbots for Mental Health and Therapy Market is expected to experience steady growth, driven by technological progress and increasing demand for accessible mental health solutions, despite facing challenges in emotional comprehension and ethical considerations.

IntroductionCaregivers of patients with end-stage kidney disease (ESKD) face significant challenges that contribute to caregiver burden, negatively impacting their physical, psychological, social, and financial well-being. With the growing prevalence of chronic diseases and an aging population, there is an urgent need for accessible and scalable solutions to detect and address caregiver burden. Artificial Intelligence (AI) chatbots using natural language processing (NLP) have shown promise in providing mental health support and monitoring through natural conversations. This study will contribute to research and clinical practice by: (1) validating a novel approach for early detection of caregiver burden through NLP, (2) analyzing the feasibility of AI-powered chatbots for continuous caregiver monitoring, and (3) informing the development of scalable, accessible tools to identify at-risk caregivers.Methods and analysisThis protocol for the mixed methods aims to evaluate the feasibility, acceptability, and preliminary effectiveness of BOTANIC (Burden Observation and Timely Aid for Navigating Informal Caregiving), an AI-powered chatbot for early detection of caregiver burden. A single-center validation study will be conducted at Alexandra Hospital, Singapore. Twenty primary caregivers of ESKD patients will be recruited to use BOTANIC for 12 weeks. BOTANIC, developed using Python and open-source libraries, will integrate with Telegram and utilize advanced NLP techniques to analyze caregiver conversations and detect signs of burden. The NLP algorithm will analyze conversations to generate burden scores at baseline and at 12 weeks. Participants will also complete baseline and 12-week assessments using validated questionnaires including the Zarit Burden Interview (ZBI), Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder-7 (GAD-7). Primary outcomes include concordance between caregiver burden levels detected by the NLP algorithm and validated assessment scores at both timepoints. Secondary outcomes include user engagement metrics and system satisfaction. Semi-structured interviews will explore participants’ experiences with the chatbot. Quantitative data will be analyzed using descriptive statistics and appropriate statistical tests such as paired t-tests or Wilcoxon signed-rank tests, while qualitative data will undergo thematic analysis.Ethics and disseminationThe study has been approved by the NHG Domain Specific Review Board. Findings will be published in peer-reviewed journals, presented at conferences, and used to inform the development of larger-scale trials of AI-powered caregiver support interventions.

Background: Several chatbots that utilize large language models now exist. As a particularly well-known example, ChatGPT employs an autoregressive modeling process to generate responses, predicting the next word based on previously derived words. Consequently, instead of deducing a correct answer, it arranges the most frequently appearing words in the learned data in order. Optimized for interactivity and content generation, it presents a smooth and plausible context, regardless of whether the content it presents is true. This report aimed to examine the reliability of ChatGPT, an artificial intelligence (AI) chatbot, in diagnosing diseases and treating patients, how to interpret its responses, and directions for future development.Current Concepts: Ten published case reports from Korea were analyzed to evaluate the efficacy of ChatGPT, which was asked to describe the correct diagnosis and treatment. ChatGPT answered 3 cases correctly after being provided with the patient’s symptoms, findings, and medical history. The accuracy rate increased to 7 out of 10 after adding laboratory, pathological, and radiological results. In one case, ChatGPT did not provide appropriate information about suitable treatment, and its response contained inappropriate content in 4 cases. In contrast, ChatGPT recommended appropriate measures in 4 cases.Discussion and Conclusion: ChatGPT’s responses to the 10 case reports could have been better. To utilize ChatGPT efficiently and appropriately, users should possess sufficient knowledge and skills to determine the validity of its responses. AI chatbots based on large language models will progress significantly, but physicians must be vigilant in using these tools in practice.

Chatbots are AI-powered tools able to assist consumers in their online shopping. A global survey from 2023 confirmed that consumers are interested in the chatbots' functionalities, with 44 percent of respondents appreciating the help of chatbots in finding product information before the actual purchase. Another 35 percent of surveyed shoppers were interested in using the customer service support available on retailers' or brands' websites.

This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.

This dataset includes FAQ data and their categories to train a chatbot specialized for e-learning system used in Tokyo Metropolitan University. We report accuracies of the chatbot in the following paper.

Yasunobu Sumikawa, Masaaki Fujiyoshi, Hisashi Hatakeyama, and Masahiro Nagai "Supporting Creation of FAQ Dataset for E-learning Chatbot", Intelligent Decision Technologies, Smart Innovation, IDT'19, Springer, 2019, to appear.

Yasunobu Sumikawa, Masaaki Fujiyoshi, Hisashi Hatakeyama, and Masahiro Nagai "An FAQ Dataset for E-learning System Used on a Japanese University", Data in Brief, Elsevier, in press.

This dataset is based on real Q&A data about how to use the e-learning system asked by students and teachers who use it in practical classes. The duration we collected the Q&A data is from April 2015 to July 2018.

We attach an English version dataset translated from the Japanese dataset to ease understanding what contents our dataset has. Note here that we did not perform any evaluations on the English version dataset; there are no results how accurate chatbots responds to questions.

File contents:

• FAQ data (*.csv)
  1. Answer2Category.csv: Categories of answers.
  2. Answer2Tag.csv: Titles of answers.
  3. Answers.csv: IDs for answers and texts of answers.
  4. Categories.csv: Names of categories for answers.
  5. Questions.csv: Texts of questions and their corresponding answer IDs.
  6. Answers_english.csv: IDs for answers and texts of answers written in English.
  7. Categories_english.csv: Names of categories for answers and their corresponding English names.
  8. Questions_english.csv: Texts of questions and their corresponding answer IDs written in English.


• Statistics (*.tsv)

  Results of statistical analyses for the dataset. We used Calinski and Harabaz method, mutual information, Jaccard Index, TF-IDF+KL divergence, and TF-IDF+JS divergence in order to measure qualities of the dataset. In the analyses, we regard each answer as a cluster for questions. We also perform the same analyses for categories by regarding them as clusters for answers.

Grants: JSPS KAKENHI Grant Number 18H01057

The global healthcare chatbots market size reached USD 352.5 Million in 2024. Looking forward, IMARC Group expects the market to reach USD 1,403.8 Million by 2033, exhibiting a growth rate (CAGR) of 16.6% during 2025-2033. The market is witnessing steady growth driven by the growing adoption of digital technologies in healthcare, the impact of the COVID-19 pandemic, rising prevalence of chronic diseases, advancements in natural language processing, regulatory support for telehealth, and increasing demand for 24/7 healthcare.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the global, regional, and country levels for 2025-2033. Our report has categorized the market based on component, deployment mode, application, and end user.